pyunit_checkpoint_new_category_in_predictorDL.py

import sys, os
sys.path.insert(1, os.path.join("..","..",".."))
import h2o
from tests import pyunit_utils
from h2o.estimators.deeplearning import H2ODeepLearningEstimator

#TODO(vlad): this test randomly fails with no meaningful messages. Tracking it.

def checkpoint_new_category_in_predictor():

  sv1 = h2o.upload_file(pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv"))
  sv2 = h2o.upload_file(pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv"))
  vir = h2o.upload_file(pyunit_utils.locate("smalldata/iris/virginica.csv"))
  print("checkpoint_new_category_in_predictor-1")
  m1 = H2ODeepLearningEstimator(epochs=100)
  m1.train(x=[0,1,2,4], y=3, training_frame=sv1)

  m2 = H2ODeepLearningEstimator(epochs=200, checkpoint=m1.model_id)
  m2.train(x=[0,1,2,4], y=3, training_frame=sv2)
  print("checkpoint_new_category_in_predictor-2")

  # attempt to continue building model, but with an expanded categorical predictor domain.
  # this should fail
  try:
    m3 = H2ODeepLearningEstimator(epochs=200, checkpoint=m1.model_id)
    m3.train(x=[0,1,2,4], y=3, training_frame=vir)
    assert False, "Expected continued model-building to fail with new categories introduced in predictor"
  except EnvironmentError:
    pass
  
  print("checkpoint_new_category_in_predictor-3")

  # attempt to predict on new model, but with observations that have expanded categorical predictor domain.
  predictions = m2.predict(vir)
  print("checkpoint_new_category_in_predictor-4")


if __name__ == "__main__":
    pyunit_utils.standalone_test(checkpoint_new_category_in_predictor)
else:
    checkpoint_new_category_in_predictor()